{"id":"W2781716426","doi":"10.1007/s11229-017-1660-0","title":"Accommodation, prediction and replication: model selection in scale construction","year":2018,"lang":"en","type":"article","venue":"Synthese","topic":"Design Education and Practice","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Exploratory factor analysis; Confirmatory factor analysis; Accommodation; Scale (ratio); Replication (statistics); Set (abstract data type); Selection (genetic algorithm); Philosophy of science; Process (computing); Computer science; Psychology; Data science; Management science; Epistemology; Artificial intelligence; Structural equation modeling; Machine learning; Mathematics; Statistics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002061138,0.00005067715,0.00004590902,0.00008649485,0.00004859107,0.00002638751,0.00002944928,0.00004861812,0.00007311045],"category_scores_gemma":[0.00008070364,0.00005707717,0.000006229191,0.0001799346,0.00003549641,0.0003612725,0.000004517869,0.0000691617,0.00003625793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004865217,"about_ca_system_score_gemma":0.00001599368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001346868,"about_ca_topic_score_gemma":0.00004220542,"domain_scores_codex":[0.9996114,0.00002416105,0.0001155673,0.0001256302,0.00005290308,0.00007037799],"domain_scores_gemma":[0.9997491,0.00003559475,0.0000208457,0.0001174001,0.00004858833,0.00002846433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001524664,0.0002170399,0.1062427,0.0001662336,0.00006616149,4.222267e-7,0.007515253,0.04230421,0.1708082,0.01117654,0.01898888,0.6423618],"study_design_scores_gemma":[0.0001371614,0.00001788178,0.02949374,0.00001903224,0.00001002539,0.00003668842,0.0002685247,0.9481405,0.01253056,0.002012799,0.007229044,0.0001040732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8553439,0.00005122419,0.1141576,0.0006134851,0.0003873397,0.00022116,0.000006588069,0.0003677309,0.02885096],"genre_scores_gemma":[0.99299,0.00004421254,0.006619093,0.00003009206,0.0001298799,0.00002975505,0.000004789318,0.000009766685,0.0001424267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9058363,"threshold_uncertainty_score":0.2327539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01522202039259954,"score_gpt":0.2552044138125351,"score_spread":0.2399823934199355,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}